System and method for automating association of retail items to support shopping proposals

ABSTRACT

A shopping server proposal system includes a mechanism that enhances the online retailer&#39;s existing database system with the necessary information to provide the desired services. The existing catalog of items is analyzed based on a set of predefined rules for a given retail store. This analysis determines which of the items in the catalog are related to other items in the catalog. The result of this analysis includes new relations of the catalog items that are written into the retailer&#39;s existing database system. The mechanism for assembling this information, based on a set of predefined rules, is independent of the retailer or the retailer&#39;s industry. The provides retail businesses with a competitive edge by enabling them to offer automated shopping advice to solve the shoppers&#39; problems of finding related and matching items, and by providing a list of related items based on the selected item&#39;s properties.

FIELD OF THE INVENTION

The present invention relates to the field of electronic commerce(e-commerce), and it particularly relates to consumer-to-businesstransactions. More specifically, in the context of this invention, abusiness can be a retail store or a group of merchants whose retailitems can be combined based on attributes such as colors, sizes, andstyle in fashion stores. The present invention is equally applicable toother stores that market, for example, hardware, tools, electronics, andother items.

BACKGROUND OF THE INVENTION

The World Wide Web (WWW) is comprised of an expansive network ofinterconnected computers upon which businesses, governments, groups, andindividuals throughout the world maintain inter-linked computer filesknown as web pages. Shoppers navigate these pages by means of computersoftware programs commonly known as Internet browsers. Due to the vastnumber of WWW sites, many web pages have a redundancy of information orshare a strong likeness in either function or title. The vastness of theunstructured WWW causes shoppers to rely primarily on Internet searchengines to retrieve information or to locate businesses. These searchengines use various means to determine the relevance of ashopper-defined search to the information retrieved.

The authors of web pages provide information known as metadata withinthe body of the hypertext markup language (HTML) document that definesthe web pages. A computer software product known as a web crawlersystematically accesses web pages by sequentially following hypertextlinks from page to page. The crawler indexes the pages for use by thesearch engines from information about a web page as provided by itsaddress or Universal Resource Locator (URL), metadata, and othercriteria found within the page. The crawler is run periodically toupdate previously stored data and to append information about newlycreated web pages. The information compiled by the crawler is stored ina metadata repository or database. The search engines search thisrepository to identify matches for the shopper-defined search ratherthan attempt to find matches in real time.

A typical search engine has an interface with a search window where theshopper enters an alphanumeric search expression or keywords. The searchengine sifts through available web sites for the shopper's search terms,and returns the search of results in the form of HTML pages. Each searchresult includes a list of individual entries that have been identifiedby the search engine as satisfying the shopper's search expression. Eachentry or “hit” may include a hyperlink that points to a Uniform ResourceLocator (URL) location or web page.

Electronic shopping (or e-shopping) has been gaining popularity as thepopularity of the World Wide Web increases. E-shopping continues toevolve from a means of providing an easy way of accessing (andpublishing) information on the Internet to a virtual marketplace wherealmost every merchandise can be traded, as it is in the physical world.As more retail businesses market their merchandise over the WWW, it willbecome more important for a business to distinguish itself from thecompetition. One of the significant deficiencies of online retail storesis the amount of shopping advice they can offer. Typically, the shopperdoes not have access to a sales clerk to accompany him or her in findingthe items of choice, or related and matching items.

For example, if the shopper is browsing in a regular real-world clothingstore he or she can ask a sales clerk for assistance in finding items.The sales clerk can make recommendations of items that may match orenhance the chosen items. This type of advice is often missing in onlineshopping stores. Certain online stores try to compensate for thisdeficiency by offering online chat rooms as an additional service.However, online chat rooms require staffing thus added operationalexpense. There is therefore a need to automate the online serviceadvice.

In addition, merchants may wish to perform “cross-selling” of goods andservices, that is selling related and associated items in addition tothe actual sale. In real-world shopping stores sales clerks are able toassist the shoppers by providing useful advice, which might result inadditional sales. For instance, a sales clerk may recommend a shirt, anda tie, which match the selected trouser. Rather than selling only thetrouser, the sales clerk will sell related additional items and increasethe merchant's sales. As pointed out earlier, an Internet-based shoppingsite does not typically have the possibility of enhancing sales byutilizing cross-selling.

The following are exemplary attempts to provide personalized services inthe field of the present invention. For a more detailed description ofthe services, reference is made to the corresponding web sites.

Broadvision provides solutions in the area of personalization, marketingand promotional tools for web sites. This company's web site enablescompanies to cross-sell items, that is, selling similar or relatedversions, or up-sell items, that is, newer versions, to shoppers basedon previous purchases in their shopping basket, and communities of whichthey are members. The main focus and emphasis of Broadvision is anend-to-end application for rapid deployment and dynamic personalizationof high transaction volume retail e-commerce sites. However, Broadvisiondoes not use a rule-based approach to automatically generate linkagebetween different articles.

Dynamo Personalization Server is a rule-driven personalization platformbased on the Dynamo Application Server. Dynamo Personalization Serverallows targeting specific content to a particular user or group of usersbased on business rules created by business managers. It combinesexplicit user data from existing marketing databases with implicitinformation gathered on user behavior, and other related sources ofinformation. According to the specifications of this product, it doesnot allow cross-selling based on rules regarding the items and does notoffer enabling technology to enhance a database system to provide retailitem associations.

The Rules-Based Merchandising engine of the Annuncio Bright productallows marketers to create a new program (for sales, marketing) based ontheir expertise. According to the product data sheet, the merchandisingengine offers the following services: It enables marketers to applytheir merchandising expertise to create successful programs. It furtherfeatures a guided rules builder and supports many criteria, such asshopper profile, product, catalog, services, content. It also encouragesmixing of criteria to create dynamic offers.

Though the merchandising engine of the Annuncio Bright product handlesthe creation of the rules, the result of the rule creation is not anassociation of items but a set of rules that define how items arerelated. Furthermore, this product provides and handles rules on thelevel of items and item categories, but not item attributes, with theitems being still associated manually. It would be desirable to have asystem and method that apply such a ruleset to an existing database.

The Blaze Advisor product focuses on the creation of “business rules”that are the basis of an application. It allows the creation of rulesdown to the level of item attributes. As with the rules-basedmerchandising engine of the Annuncio Bright product, which was discussedearlier, the result of the Blaze Advisor rule creation does notautomatically enhance a database. Furthermore, since the rules createdby the Blaze Advisor product are used during runtime, they are notindependent of the underlying database system. As used herein,“runtime”, means the rules are applied while the program is executed.The alternative would be to precompute the result of applying theruleset and then access its results during runtime. There is thereforestill a need for a method that performs a pre-computation to writeassociation information into a database such that the related items canbe easily found and accessed during runtime.

SUMMARY OF THE INVENTION

It is one feature of the present invention to enable online retailbusinesses to offer automated online shopping advice based on theshopper's current browse or for-purchase selection. An online shoppingsite (or server) can make suggestions of the best match items for theshoppers' current browse or for-purchase selections, potentiallyeliminating the need for personalized customer service, online chatadvice, or store assistants. To this end, the system and method of thepresent invention automatically produce web pages or content thatenhance the potential sale of selected items by associating otherproducts that are linked via a pre-determined rule set, thus enhancingthe shoppers' purchasing experience.

It is another feature of the present invention to enable online retailbusinesses to offer online shopping advice based on the shoppers'current browse or for-purchase selections. The online shopping site orserver can suggest items that appropriately match or enhance theshoppers' current browse or for-purchase selection. To this end, thepresent system and method focus on the automated association of items.As used herein an item can be described or characterized by a set ofattributes. An existing rule set describes how items can be combined.The automated association enables retailer shopping web sites to offershopping proposals based on these associations. The present inventiondescribes how the retailers' database system can be enhanced andmodified to support the shopping proposal application, independent ofthe retailer, the retailer's industry or the retailer's database system.

The foregoing and other features and advantages of the present inventionare realized by a shopping server proposal system and method that aregenerally comprised of a mechanism that enhances the online retailer'sexisting database system with the necessary information to provide thedesired services. The existing catalog of items is analyzed based on aset of predefined rules for a given retail store (e.g. furniture,clothing, electronics, etc.). This analysis determines which of theitems in the catalog are related to other items in the catalog. Forexample, one of the rules may define that any outdoor clothing item formen that is not underwear, can be combined with any other outdoorclothing item for men if the colors of the items match. The result ofthis analysis is a new set of relations of catalog items including a setof properties (e.g., color, size, category, etc.). These relations arewritten into the retailer's existing database system. The mechanism forassembling this information, based on a set of predefined rules, isindependent of the retailer or the retailer's industry (e.g., furniture,clothing, electronics, etc.) by using intermediate formats. However,retailer specific customizations may be necessary.

The system utilizes additional information available during a shopper'sshopping experience, i.e., when the shopper browses the online store andviews a selection, to enable the online retailer to provide additionalinformation to the shopper based on the user's browsing history orprevious known online events of the same user. For example, if theshopper selects a shirt from the retailer's list of items, theretailer's server may return a web page containing information about theselected item. On a conventional web page rendering, the page onlycontains links to the retailer's catalog. However, according to thepresent invention, the page can contain information about related items,such as a matching pair of trousers, socks, etc. This additionalinformation can be presented in a variety of ways. For example, thisinformation can be incorporated into the Web page with the focus on theoriginal selected item, or additional windows or animation can be used,to enhance the presentation of the additional information.

The present system and method provide the retail businesses with acompetitive edge by enabling them to offer automated shopping advice tosolve the users' problems of finding related and matching items, and byproviding a list of related items based on the selected item'sproperties (e.g., color, size, etc.). The system and method are expectedto increase sales as shoppers are provided with additional opportunitiesto select and buy items.

Buyers are more likely to return to a site if they enjoy the shoppingexperience. Using the present system, shoppers are made aware of itemsthey may not encounter in conventional online stores, by manuallybrowsing and searching the inventory. Customer satisfaction is verylikely to increase which will have a positive effect on the onlinetraffic.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features of the present invention and the manner ofattaining them will be described in greater detail with reference to thefollowing description, claims, and drawings, wherein reference numeralsare reused, where appropriate, to indicate a correspondence between thereferenced items, and wherein:

FIG. 1 is a high level block diagram of an exemplary overall environmentin which a shopping server proposal system of the present invention maybe used;

FIG. 2 is a book diagram depicting the shopping server proposal systemof FIG. 1 in relation to an Internet service provider, a graphical userinterface, and the World Wide Web;

FIG. 3 is a block diagram provides a more detailed illustration of aretail online server shown using shopping server proposal system of FIG.1 for the creation of shopping advisor knowledge base from an existingretailer's database and an existing ruleset;

FIG. 4 illustrates an exemplary use of the shopping advisor knowledgebase of the retail online server of FIG. 3 and

FIG. 5 is a flow chart illustrating the creation of the shopping advisorknowledge base of FIG. 3; and

FIG. 6 is a flow chart illustration the use and operation of theshopping server proposal system of the foregoing figures including theshopping advisor knowledge base of FIG. 5.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following definitions and explanations provide backgroundinformation pertaining to the technical field of the present invention,and are intended to facilitate the understanding of the presentinvention without limiting its scope:

Crawler: A program that automatically explores the World Wide Web byretrieving a document and recursively retrieving some or all thedocuments that are linked to it.

Dictionary: A database of context-related terms.

E-business, e-shopping, or e-commerce transactions: Businesstransactions conducted online using the Internet or anothercommunications network.

HTML (Hypertext Markup Language): A standard language for attachingpresentation and linking attributes to informational content withindocuments. During a document authoring stage, HTML “tags” are embeddedwithin the informational content of the document. When the web document(or “HTML document”) is subsequently transmitted by a web server to aweb browser, the tags are interpreted by the browser and used to parseand display the document. In addition to specifying how the web browseris to display the document, HTML tags can be used to create hyperlinksto other web documents.

Internet: A collection of interconnected public and private computernetworks that are linked together with routers by a set of standardsprotocols to form a global, distributed network.

Retailer or merchant: Includes for example, a merchant, retailer,wholesaler, distributor, or any appropriate person in the chain ofcommerce.

Search engine: A remotely accessible World Wide Web tool that allowsusers to conduct keyword searches for information on the Internet.

Server: A software program or a computer that responds to requests froma web browser by returning (“serving”) web documents.

URL (Uniform Resource Locator): A unique address that fully specifiesthe location of a content object on the Internet. The general format ofa URL is protocol://server-address/path/filename.

Web browser: A software program that allows users to request and readhypertext documents. The browser gives some means of viewing thecontents of web documents and of navigating from one document toanother.

Web document or page: A collection of data available on the World WideWeb and identified by a URL. In the simplest, most common case, a webpage is a file written in HTML and stored on a web server. It ispossible for the server to generate pages dynamically in response to arequest from the user. A web page can be in any format that the browseror a helper application can display. The format is transmitted as partof the headers of the response as a MIME type, e.g. “text/html”,“image/gif”. An HTML web page will typically refer to other web pagesand Internet resources by including hypertext links.

Web site: A database or other collection of inter-linked hypertextdocuments (“web documents” or “web pages”) and associated data entities,which is accessible via a computer network, and which forms part of alarger, distributed informational system such as the WWW. In general, aweb site corresponds to a particular Internet domain name, and includesthe content of a particular organization. Other types of web sites mayinclude, for example, a hypertext database of a corporate “intranet”(i.e., an internal network which uses standard Internet protocols), or asite of a hypertext system that uses document retrieval protocols otherthan those of the WWW.

World Wide Web (WWW, Web): An Internet client-server hypertextdistributed information retrieval system.

FIG. 1 portrays an exemplary overall environment in which a shoppingserver proposal system 10 of the present invention may be used. Thesystem 10 includes a software or computer program product which istypically embedded within, or installed on, a host server 15. The system10 may include several host servers 15 that are dispersed geographicallyto co-ordinate the reduction of access time from online shoppers,customers, or users 35, 37, 39. Host servers 15 may be owned andmaintained by the online retail outlets or they may alternatively becontracted by the online retail outlets to third party serviceproviders.

The cloud-like communication network 20, which is represented as a cloudto indicate an indeterminate number of connections, is comprised ofcommunication lines and switches connecting servers 25 and 27 togateways 30. The servers 25, 27 and the gateway 30 provide thecommunication access to the WWW. Shoppers located at remote Internetsites, are represented by a variety of computers such as 35, 37 and 39,and can query the host server 15 for the desired information.

The host server 15 is connected to the network 20 via a communicationslink such as a telephone, cable, or satellite link. The servers 25 and27 can be connected via high-speed Internet network lines or links 44and 46 to other computers and gateways. The servers 25 and 27 provideaccess to stored information such as hypertext or web documentsindicated generally at 50. The hypertext documents 50 most likelyinclude embedded hypertext links to other locally stored pages and mayalso contain information such as location of stores, retail outlets,malls, etc.

In addition, while the system 10 is described in connection with theWWW, it can also be used with a stand-alone database of computers 35,servers 25, gateways 30, and mobile computing devices 38 forapplications that do not require interaction with the WWW. The mobilecomputing unit 38 can be a handheld set designed for the application ofthis invention or it could be a personal digital assistant (PDA) with adownloaded software application suited to implementing the method of thepresent invention. A mobile phone 199 can also be used as a mobilecomputing unit for the purposes of this invention. The increasing trendto combine a personal digital assistant 38 and cell phone 199facilitates the convenience of using satellite communications tointeract with the user. A satellite 80 be used to establishcommunication between the shoppers 35, 37, 39, servers 25, gateways 30,and the system 10.

FIG. 2 illustrates the system 10 of FIG. 1 in relation to an Internetservice provider 100, a shopper indicated by a browser or graphical userinterface (GUI) 140, and the WWW 20. A history session logging system144 records all the actions performed by the shopper while shopping atone or more retail online servers, i.e., 300, 305, 310.

A proxy server 221 can optionally be used in conjunction with the system10 and the history session logging system 144 as an interface betweenthe service provider 100 and the system 10. In this illustration, theproxy server 221 is shown implemented by the service provider 100 inorder to protect the shoppers' private information from unauthorizedhacking. It should however be understood that from a technical aspect,the proxy server 221 and the system 10 can be integrated into a singleapplication or software program, and can reside, for example, either onthe server of the service provider 100 or on an independent server 15.

As shown, the server 15 is not limited to a single retail online server300, but can service a multitude of other servers, i.e., 305, 310. Theserver 15 can be part of the retail online servers 300, 305, 310, partof service provider 100, or part of an independent service.

Searches on the WWW 20 are performed by the search service provider 100that generally comprises a web crawler 200, a search engine repository210, an indexing engine 220, a query transformer 230, a search engine240, a search results transformer 250, and an indexed data repository260. In use, the crawler 200 crawls the WWW 20 and downloads webdocuments to the search engine repository 210 where they are stored andupdated systematically. The abstract/indexing engine 220 indexes the webdocuments and generates abstracts for the documents. The abstracts andthe indexed data are stored in the abstracts/indexed data repository 260for later use by the search engine 240, as appropriate.

The search engine repository 210 is a data store maintained by a webinformation gatherer such as the web crawler 200. The search enginerepository 210 maintains information or metadata from previouslyencountered web pages. This metadata is used by the indexing engine 220to prepare the index. Preferably, the search engine repository 210 ismaintained centrally by the search service provider 100. Alternatively,the search engine repository 210 may be located and maintained on anindependently provided system to which the search service provider 100has access. The indexing engine 220 generates a description for each webdocument from the metadata stored in the search engine repository 210.The query transformer 230, prompted by the browser 140, applies aninternal query request to the indexed data stored in the indexed datarepository 260, and generates a search result with matches (or queryresults) 270 that are specific to the user's query.

In one embodiment, the system 10 and/or the history session loggingsystem 144 record the shopper's actions during a visit to a currentretail online server, i.e., 300, and optionally the shopper's actionsduring visits to other sites prior to browsing the server 300. Theshopper's actions include for example, the queries made by the shopper,the URLs visited by the shopper, the products and/or services purchasedby the shopper, the quotes requested by the shopper, the prices providedto the shopper, individual session profiles, etc. In addition, theshopper can manually enter additional information about himself orherself, such as hobbies, resume information, etc.

The bulk of this information is generally referred to herein as “shopperprofile,” and can be saved on the shopper's computer, i.e., 35, forprivacy reason, or, if authorized by the shopper, it can be saved on asecure site such as a dedicated repository provided by the serviceprovider 100, or on an independently maintained server. The informationforming the shopper profile is indexed by an indexing engine for ease ofaccess. The shopper profile can be saved for either a short time, suchas the duration of the session to the current server 300, or for anextended period of time for use in future sessions.

FIGS. 3 and 5 provide a more detailed illustration of an exemplaryretail online server 300 that utilizes the system 10 for the creation ofa shopping advisor knowledge base 400 from an existing retailer'sdatabase 350 and an existing ruleset 360. The retailer database system350 is a system that is conventionally used by the retailer's web siteapplication to offer online shopping services. For simplicity, it isassumed that the retailer database system 350 is a relational databasesystem. It should however be understood that the present invention canbe adapted to work with other types of database systems, e.g. objectoriented database systems.

The retailer's relational database system 350 is used during web siteoperations to provide shoppers with information about offered items andthe available inventory. Data types which can consist of numericalidentifiers and descriptive labels, may be defined by the followingTables 1-5, for an online clothing store:

TABLE 1 Database table (TYPES) containing item descriptions Type # Typedescription Categories 0 T-shirt 2, 3 1 Short Sleeve shirt 2, 3 2 Dress6, 7 . . . . . . . . .

TABLE 2 Database table (CATEGORIES) containing category descriptionsCategory # Category description 0 Sleepwear 1 Underwear 2 Sportswear . .. . . .

TABLE 3 Database table (COLORS) containing color descriptions Color #Color description 0 White 1 Blue 2 Yellow . . . . . .

TABLE 4 Database table (SIZES) containing size descriptions Size # SizeDescription 0 S - Small 1 M - Medium 2 L - Large . . . . . .

TABLE 5 Database table (ITEMS) containing items and their availability.Item # Type # Color Size Price ($) Availability 0 0 0 0 30.00 100 0 0 10 30.00 56 0 0 0 1 35.00 50 . . . . . . . . . . . . . . . . . .

Tables 1 to 5 are stored in the retailer's database system 350, andrepresent a simplified database schema. For ease of description, Tables1 to 5 omit the normalization and efficiency issues. In this example,Table 1 contains data type “TYPES” that includes both a number and asubcategory description. For example, the number 1 and the subcategory“short sleeves shirt”, or the number 2 and the subcategory “long sleeveshirts”.

Similarly, Table 2 contains data type “CATEGORY” that includes both anumber and a category description. For example, the number 1 andcategory description “sleepwear”, or the number 2 and the category“everyday wear”. Table 3 contains data type “COLOR” that includes both anumber and a color descriptor. For example, the number 1 and the colorred, or the number 2 and the color blue. Table 4 contains data type“SIZE” that includes both a number and a size description. For example,the number 1 and the size description “small”, or the number 2 and thesize description “medium”. Table 5 contains data type “PRICE” thatincludes only a number. For example, the value of the price “35.50”, or“99.99”. In addition, data type “AVAILABILITY” contains only a number.For example, the number 25 indicates that 25 items are available instock.

In this simplified retailer's database system 350, the labels includenumbers and descriptors, and the schema is meant to be illustrative of abasic setup. It should be clear that more elaborate schemes can bereadily developed. In this embodiment, the descriptors are used todemonstrate what kind of information is available in a retailer'sdatabase system 350. The entire information in the database 350 isstored using a schema which defines relations, fields and keys.

Software applications, fed by queries from the shoppers' web browsers140, can access the elements (e.g. fields, keys, values) of theretailer's database system 350, by using SQL (a standard querylanguage). For example: a shopping server application requests that allavailable items in size L (large) for a price lower than $50.00 bedisplayed. The shopper's web browser 140 passes information to theshopping server application, which accesses the database 350 by issuinga logical SQL query similar to the following:

-   -   “SELECT*FROM ITEMS WHERE PRICE<50.00 AND SIZE=2 AND AVAILABILITY        >0”

The database 350 responds with a subset of items, evaluated from thedescriptors, which meet the requirements of the SQL query. The shoppingserver application can then encode this list in HTML and send it to theshopper's web browser 140.

As used herein, a ruleset 360 includes a number of rules that define therelated items under certain conditions. For example, rule A of ruleset360 may be as follows: if the item is a shirt then related items arepants, skirts, etc. As another example, rule B of ruleset 360 may be asfollows: if the item has the color red then related items have thecolors blue, black or white.

A rule generally includes two parts: the evidence and the conclusion. Asan example, in rule B above, if it is assumed that an item has the colorred, it is concluded that related items must have the color black, blueor white. Such a rule may be provided in a machine readable (e.g. XML)form to a terminology conversion module 371. It is assumed here that theruleset 360 is industry dependent but is reusable for retailers of thesame industry. Retailer specific extensions of the ruleset 360 areoptional and do not affect the underlying invention.

The shopping server proposal system 10 is generally comprised of aterminology conversion module 371 that communicates with the retailer'sdatabase system 350 and takes as input the ruleset 360. The terminologyconversion module 371 is responsible for providing an analysis andrelation creation module 390 with appropriate input. In turn, theanalysis and relation creation module 390 updates a knowledge base 400.

Since retailers may use a variety of database systems 350, and numerousvendors for database applications, the system 10 is capable ofsupporting heterogeneous terminologies, that is various naming-schemes,used to access and retrieve information from the database systems 350.As an example, even though two retailers in the same industry use thesame database system 350, the tables and values in the database systems350 can be named differently, but remain nonetheless accessible by thesystem 10.

An important function of the terminology conversion module 371 is toassociate the terminology of the retailer independent ruleset 360 withthe schema terminology of the retailer database system 350. The schemaof the database system 350 may be retailer specific, depending on thetype of database system 350 or the manufacturer of the databaseapplication.

The function of the terminology conversion module 371 can be generallydescribed by the following iterative process, and is illustrated byblock or step 615 of the method 610 of creating the shopping advisorknowledge base 400 (FIG. 5):

-   -   create a list of each term used in the ruleset 360 (list_1);    -   create an (initially empty) list of term mappings (list_2);    -   for each term in list_1, perform the following: perform a lookup        of the ruleset term; associate the ruleset term with a        corresponding term in the schema of the database system 350; and        store the new association in list_2; and    -   serialize list_2 into machine readable form.

The step of looking up the ruleset term can be implemented in severalways. According to one implementation, the terminology conversion module371 can perform the lookup interactively, i.e., the administrator of thedatabase system 350 may select an element (e.g., a field or a value)from the database system 350 for each of the terms of the ruleset 360.

According to another implementation, the retailer provides the mappingsexternally, as additional input to the terminology conversion module371. This could be a simple list of term types, such as: item, article;XL, Extra Large.

Another implementation is an automated association using heuristics,such as where a term in the database system 350 has the same meaning asa term in the ruleset 360 with the same name. Though the foregoingimplementations have been described separately, it should be clear thatthese techniques can also be combined. As an example, user interactionmay be requested when a term of either the ruleset 360 or the databasesystem 350 cannot be found in the database or the ruleset respectively.

The output of the terminology conversion module 371 can be, for example,in an intermediary format, such as a XML document 380 that describes themappings of the terms of the database system 350 to the terms of theruleset 360, as well as database specific information about field names.The following is an exemplary output document 380:

<DATABASE_RULESET_MAPPING> <MAPPING> <DB_TERM> <FIELD_TERM value=“Item”table=“ITEMS”/> </DB_TERM> <RULESET_TERM value=“Article”/> </MAPPING><MAPPING> <DB_TERM> <FIELD_TERM value=“Color” table=“COLORS”/></DB_TERM> <RULESET_TERM value=“Color”/> </MAPPING> <MAPPING> <DB_TERM><VALUE_TERM value=“red” field =“Color description” table=“COLORS”/><RULESET_TERM value=“Color”/> </MAPPING> . . .</DATABASE_RULESET_MAPPING>

By providing a generic output document 380, the analysis and relationcreation module 390 can be independent of the retailer and theunderlying database system 350.

The analysis and relation creation module 390 analyses the ruleset 360at block 619 of FIG. 5, and creates the shopping advisor knowledge base400. This shopping advisor knowledge base 400 is represented as a set ofrelations in the existing database system 350, that is the output ofanalysis and relation creation module 390 is an extension of theretailer's exiting database system 350.

The input to the analysis and relation creation module 390 includes theoutput of the terminology conversion module 371 and the ruleset 360. Animportant objective of the analysis and relation creation module 390 isto discover which items offered by a retailer are related to an itemselected by the shopper, based on the ruleset 360 and the data in thedatabase system 350. As used herein, the term “item” refers to aparticular piece, such as a shirt, and its distinguishing properties,such as color, size, price, etc.

The process of analyzing the ruleset 360 and creating the shoppingadvisor knowledge base 400 includes the following steps:

-   -   create a mapping list to store references to all the related        items of an item;    -   for each item (referred to as the current item later on) of the        retailer database system 350 perform the following steps:        -   search the terms from the ruleset 360 for terms that are            applicable to the current item;        -   if any of the rules are applicable, combine the applicable            rules and create an SQL statement; and if the result set of            this statement is not empty, create a list of references to            all the related items of the result set, and store a mapping            of {current item, list of references} in the mapping list;            and    -   create a new database table to hold the mappings for each item        and its related items, based on the entries of the mapping list.

As shown in Table 1 above, an item is a record in the database system350 with a set of attributes, with the attributes being the field namesand values. The rules in the ruleset 360 specify conditions that referto these fields. A rule is applicable to an item if the item satisfiesthe evidence part of the term from ruleset 360. For example, it isassumed for illustration purposes, that two applicable rules D and Ewere found for an item X (a red shirt). Rule D may specify that if anitem is a shirt (the evidence), all items that are either long trousersor skirts or shorts (the conclusion) are related items. Rule E mayspecify that if an item has the color red, related items must have thecolors black, blue or white. The item X matches both evidences andtherefore the two rules D and E are applicable.

Once all the applicable rules (i.e., D and E) are identified, theanalysis and relation creation module 390 identifies the items in theretailer's database that can be considered to be related items. As usedherein, a “related item” satisfies all the conclusions of each of theapplicable rules. In the example above, assume that two applicable ruleswere found for the current item (i.e., a red shirt). The first rule is:if the item is a shirt then the related items must be long trousers,skirts, or shorts. The second rule is: if the item has the color redthen the related items must have the colors blue, black or white. Ittherefore follows that all items in the database system 350 that areskirts, long trousers, or shorts (conclusion of the first rule), withone of the colors concluded by the second rule, are considered relateditems.

The related items can be readily determined by combining the conclusionsof all applicable rules into an SQL statement of, for example, thefollowing form:

-   SELECT “Item #” FROM Table.ITEMS WHERE Type=“long trousers” OR    Type=“shorts” OR Type=“skirt”)-   AND(Color=“black” OR Color=“blue” OR Color=“white”)

The results of this SQL query is a list of item numbers that arereferences to the related items. This information can easily be storedin a new database table (i.e., the knowledge base 400) as part of, or anextension of the database system 350.

As explained earlier, the output of the analysis and relation creationmodule 390 is a new database table or the knowledge base 400 that iscommunicated and written to the existing retailer database system 350.Based on the entries in the mapping list, the new knowledge base 400 canbe of conjoined item numbers and related item references, and canassume, for example, the form in the following Table 6:

TABLE 6 Result database table (RELATED_ITEMS) tem # Related Items 0 1,2, 3 1 4, 5, 6 2 2, 6 . . . . . .

The process of creating the knowledge base 400 is done once, and mayonly have to be repeated when either the schema of the database system350 or the ruleset 360 changes. Having described the creation of theknowledge base 400, its use will now be described in connection withFIG. 4.

FIGS. 4 and 6 illustrate an exemplary usage, or a method of use 650, ofthe knowledge base 400. A shopper 35 browses an online retailer shoppingsite 300 and selects an item to look at or to buy at block 510. Insteadof having the shopping site 300 simply return a web page containinginformation about the selected item, the shopping server 300 can nowinclude information about related items.

To this end, the shopping server 300 is provided with three modules: Arequest analysis module 500, a relation finder module 525, and aresponse creation module 550. The request analysis module 500 receivesincoming requests 510 from the shoppers 35 and identifies the currentlyselected item or items. This information is extracted from the request(step 652), which is typically an HTTP GET or POST request. The request510 contains all the necessary pieces of information, i.e., item number,attributes, etc., which uniquely identify the user's current selection.The request analysis module 500 extracts that information, at block 652,and delivers this information 653 to the relation finder module 525. Itshould be noted here that, depending on the underlying implementation,the request may not contain all information about selected item but justa simple reference. In such a case, the above mentioned process ofextracting the information about the selected item 652 may access thedatabase. For the purpose of clarity we omitted this in FIG. 6.

The relation finder module 525 issues an SQL statement, based on theinput from the request analysis module 500. The result of this SQLstatement is the set of the related items 657 from the database system350 (FIG. 5). This result set includes references to the related items657 and to all the attributes of the related items 657, and is deliveredto the response creation module 550.

The response creation module 550 receives the result set of relateditems and attributes and the originally selected items, and creates, atblock 660 (FIG. 6), a dynamic response 600 to the shopper's request 510.This response can be, for example, an HTML page that is rendered anddisplayed by the shopper 35 using the web browser or user interface 140(FIG. 3). This dynamically created HTML page focuses on the originallyselected item but also offers advice to the user about the relateditems. There are several ways to provide this advice. One possibility isto provide a highly visible button for the user to press to reviewrelated items. Another possibility is the use of additional windows andframes. Other possibilities include animation, audio, and/or videoattachments. However this implementation depends on the retailer'spreferences and the available technology on the shopper's side.

It is to be understood that the specific embodiments of the inventionthat have been described are merely illustrative of certain applicationof the principle of the present invention. Numerous modifications may bemade to the shopping server proposal system 10 and associated methoddescribed herein without departing from the spirit and scope of thepresent invention. For example, the system 10 can provide advice in theform of a recommendation.

As used herein, the term advice is based on the fact that allassociations between items are created based on the ruleset. The rulesetis defined by one or more experts in a certain fields such as fashion orelectronics, and will lead to associations that are reasonable andappropriate in terms of the respective field. As a result every relateditem can be understood as an advice or a recommendation to combine thecurrently selected item with any one or more of the related items.

With regards to the personalization aspect, if a user's profile isknown, the user's preferences in terms of colors sizes and prices couldbe combined with the present invention but is not precomputed with theruleset.

1. A method of providing a shopping proposal that enhances a merchant'sexisting database system, comprising: analyzing a catalog of items inthe existing database system based on a set of predefined rules thatcorrelate the items under certain conditions, to determine which of theitems in the catalog are related to other items in the catalog, and todefine a new set of relations between the catalog items; wherein eachrule comprises an evidence and a conclusion, and leads to newassociations between the catalog items; applying the new set ofrelations to the existing database system to update the database systemby providing the new associations of the items in the database system,applying the new set of relations includes associating terminology of aretailer independent ruleset with schema terminology of a retailerdatabase system by mapping terms from the retailer independent rulesetto terms in the retailer database system and serializing the mapped listinto machine readable form; generating a shopping advisor knowledgedatabase that comprises the new associations for each item of theexisting database system; and offering automated, dynamic, andpersonalized shopping advice to the shopper based on a shopper query, byretrieving the new associations in the shopping advisor knowledgedatabase, and items from the existing database system that have beenrelated by the new associations.
 2. The method according to claim 1,wherein analyzing the catalog of items in the existing database systemis based on a set of predefined rules for a given line of items.
 3. Themethod according to claim 1, wherein defining the new set of relationsincludes defining a set of properties for the catalog items.
 4. Themethod according to claim 3, wherein defining a set of propertiesincludes defining any one or more of color, size, or category.
 5. Themethod according to claim 4, wherein applying the new set of relationsincludes assembling catalog items based on a set of predefined rulesthat is independent of the merchant's industry.
 6. The method accordingto claim 5, wherein applying the new set of relations includes using anintermediate format to list items that have been related by the newassociations.
 7. The method according to claim 1, further includingusing additional information available during a shopping session.
 8. Themethod according to claim 7, wherein using additional informationincludes using information based on any one or more of: the shopper'sbrowsing history or previous purchases.